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<div class="title">our_cvfh.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *  Copyright (c) 2012-, Open Perception, Inc.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *  are met:</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
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<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> *     copyright notice, this list of conditions and the following</span></div>
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<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> *     with the distribution.</span></div>
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<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> *     from this software without specific prior written permission.</span></div>
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<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="comment"> *  POSSIBILITY OF SUCH DAMAGE.</span></div>
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<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="comment"> * $Id: cvfh.hpp 5311 2012-03-26 22:02:04Z aaldoma $</span></div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#ifndef PCL_FEATURES_IMPL_OURCVFH_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#define PCL_FEATURES_IMPL_OURCVFH_H_</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/features/our_cvfh.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/features/vfh.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;pcl/features/normal_3d.h&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;pcl/features/pfh_tools.h&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">#include &lt;pcl/common/transforms.h&gt;</span></div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a75bbfd331a8bff6f0af23348afc5fe26">   52</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a75bbfd331a8bff6f0af23348afc5fe26">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::compute</a> (<a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;{</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_feature.html">Feature&lt;PointInT, PointOutT&gt;::initCompute</a> ())</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  {</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  }</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  <span class="comment">// Resize the output dataset</span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;  <span class="comment">// Important! We should only allocate precisely how many elements we will need, otherwise</span></div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="comment">// we risk at pre-allocating too much memory which could lead to bad_alloc</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  <span class="comment">// (see http://dev.pointclouds.org/issues/657)</span></div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (1);</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  <span class="comment">// Perform the actual feature computation</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  computeFeature (output);</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160; </div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  <a class="code" href="classpcl_1_1_feature.html">Feature&lt;PointInT, PointOutT&gt;::deinitCompute</a> ();</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;}</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00075"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a9b8d59946380129bb1452193d7629705">   75</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a9b8d59946380129bb1452193d7629705">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::extractEuclideanClustersSmooth</a> (<span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> &amp;cloud,</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;                                                                                        <span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> &amp;normals,</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;                                                                                        <span class="keywordtype">float</span> tolerance,</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;                                                                                        <span class="keyword">const</span> pcl::search::Search&lt;pcl::PointNormal&gt;::Ptr &amp;tree,</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;                                                                                        std::vector&lt;pcl::PointIndices&gt; &amp;clusters, <span class="keywordtype">double</span> eps_angle,</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;                                                                                        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster,</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;                                                                                        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster)</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;{</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  <span class="keywordflow">if</span> (tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">getInputCloud</a> ()-&gt;points.size () != cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  {</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n&quot;</span>, tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">getInputCloud</a> ()-&gt;points.size (), cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  }</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () != normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  {</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Number of points in the input point cloud (%lu) different than normals (%lu)!\n&quot;</span>, cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  }</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;  <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  std::vector&lt;bool&gt; processed (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160; </div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;  std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;  <span class="comment">// Process all points in the indices vector</span></div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ()); ++i)</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  {</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    <span class="keywordflow">if</span> (processed[i])</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160; </div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    std::vector&lt;unsigned int&gt; seed_queue;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <span class="keywordtype">int</span> sq_idx = 0;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    seed_queue.push_back (i);</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    processed[i] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160; </div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordflow">while</span> (sq_idx &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (seed_queue.size ()))</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    {</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;      <span class="comment">// Search for sq_idx</span></div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      <span class="keywordflow">if</span> (!tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">radiusSearch</a> (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      {</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;      }</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 1; j &lt; nn_indices.size (); ++j) <span class="comment">// nn_indices[0] should be sq_idx</span></div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;      {</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        <span class="keywordflow">if</span> (processed[nn_indices[j]]) <span class="comment">// Has this point been processed before ?</span></div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160; </div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;        <span class="comment">//processed[nn_indices[j]] = true;</span></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        <span class="comment">// [-1;1]</span></div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160; </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;        <span class="keywordtype">double</span> dot_p = normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[0] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[0]</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;            + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[1] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[1] + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[2]</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;            * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[2];</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <span class="keywordflow">if</span> (fabs (acos (dot_p)) &lt; eps_angle)</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        {</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;          processed[nn_indices[j]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;          seed_queue.push_back (nn_indices[j]);</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;      }</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;      sq_idx++;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    }</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="comment">// If this queue is satisfactory, add to the clusters</span></div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keywordflow">if</span> (seed_queue.size () &gt;= min_pts_per_cluster &amp;&amp; seed_queue.size () &lt;= max_pts_per_cluster)</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> r;</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;      r.indices.resize (seed_queue.size ());</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; seed_queue.size (); ++j)</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        r.indices[j] = seed_queue[j];</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160; </div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;      std::sort (r.indices.begin (), r.indices.end ());</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      r.header = cloud.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a>;</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;      clusters.push_back (r); <span class="comment">// We could avoid a copy by working directly in the vector</span></div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    }</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;  }</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;}</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; </div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a67094d15b4a0e315a1e70294562ec8a7">  161</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a67094d15b4a0e315a1e70294562ec8a7">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::filterNormalsWithHighCurvature</a> (<span class="keyword">const</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointNT&gt;</a> &amp; cloud,</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                                                                                        std::vector&lt;int&gt; &amp;indices_to_use,</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;                                                                                        std::vector&lt;int&gt; &amp;indices_out, std::vector&lt;int&gt; &amp;indices_in,</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;                                                                                        <span class="keywordtype">float</span> threshold)</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;{</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;  indices_out.resize (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  indices_in.resize (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="keywordtype">size_t</span> in, out;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  in = out = 0;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160; </div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (indices_to_use.size ()); i++)</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  {</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[indices_to_use[i]].curvature &gt; threshold)</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    {</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      indices_out[out] = indices_to_use[i];</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      out++;</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    }</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;      indices_in[in] = indices_to_use[i];</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;      in++;</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    }</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;  }</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160; </div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;  indices_out.resize (out);</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  indices_in.resize (in);</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;}</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160; </div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00191"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a25128a5ac4a93f7a7e520f6e70e0abd2">  191</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a25128a5ac4a93f7a7e520f6e70e0abd2">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::sgurf</a> (Eigen::Vector3f &amp; centroid, Eigen::Vector3f &amp; normal_centroid,</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                                                               PointInTPtr &amp; processed, std::vector&lt;Eigen::Matrix4f, Eigen::aligned_allocator&lt;Eigen::Matrix4f&gt; &gt; &amp; transformations,</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;                                                               PointInTPtr &amp; grid, <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> &amp; indices)</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;{</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160; </div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  Eigen::Vector3f plane_normal;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  plane_normal[0] = -centroid[0];</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  plane_normal[1] = -centroid[1];</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  plane_normal[2] = -centroid[2];</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  plane_normal.normalize ();</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;  Eigen::Vector3f axis = plane_normal.cross (z_vector);</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  <span class="keywordtype">double</span> rotation = -asin (axis.norm ());</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  axis.normalize ();</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  Eigen::Affine3f transformPC (Eigen::AngleAxisf (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (rotation), axis));</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;  grid-&gt;points.resize (processed-&gt;points.size ());</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; processed-&gt;points.size (); k++)</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    grid-&gt;points[k].getVector4fMap () = processed-&gt;points[k].getVector4fMap ();</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160; </div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a> (*grid, *grid, transformPC);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  Eigen::Vector4f centroid4f (centroid[0], centroid[1], centroid[2], 0);</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  Eigen::Vector4f normal_centroid4f (normal_centroid[0], normal_centroid[1], normal_centroid[2], 0);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160; </div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  centroid4f = transformPC * centroid4f;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  normal_centroid4f = transformPC * normal_centroid4f;</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160; </div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  Eigen::Vector3f centroid3f (centroid4f[0], centroid4f[1], centroid4f[2]);</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  Eigen::Vector4f farthest_away;</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  <a class="code" href="group__common.html#ga1583a71aef0f54550adef0ebfef89edd">pcl::getMaxDistance</a> (*grid, indices.indices, centroid4f, farthest_away);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;  farthest_away[3] = 0;</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160; </div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  <span class="keywordtype">float</span> max_dist = (farthest_away - centroid4f).norm ();</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; </div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;  <a class="code" href="group__common.html#ga7f82fbd4e17063ab86287a2543bdea88">pcl::demeanPointCloud</a> (*grid, centroid4f, *grid);</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160; </div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  Eigen::Matrix4f center_mat;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;  center_mat.setIdentity (4, 4);</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;  center_mat (0, 3) = -centroid4f[0];</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;  center_mat (1, 3) = -centroid4f[1];</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;  center_mat (2, 3) = -centroid4f[2];</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160; </div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;  Eigen::Matrix3f scatter;</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;  scatter.setZero ();</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  <span class="keywordtype">float</span> sum_w = 0.f;</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;  <span class="comment">//for (int k = 0; k &lt; static_cast&lt;intgrid-&gt;points[k].getVector3fMap ();&gt; (grid-&gt;points.size ()); k++)</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; static_cast&lt;int&gt; (indices.indices.size ()); k++)</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;  {</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    Eigen::Vector3f pvector = grid-&gt;points[indices.indices[k]].getVector3fMap ();</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    <span class="keywordtype">float</span> d_k = (pvector).norm ();</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordtype">float</span> w = (max_dist - d_k);</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;    Eigen::Vector3f diff = (pvector);</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    Eigen::Matrix3f mat = diff * diff.transpose ();</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    scatter = scatter + mat * w;</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    sum_w += w;</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;  }</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;  scatter /= sum_w;</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160; </div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;  Eigen::JacobiSVD &lt;Eigen::MatrixXf&gt; svd (scatter, Eigen::ComputeFullV);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;  Eigen::Vector3f evx = svd.matrixV ().col (0);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;  Eigen::Vector3f evy = svd.matrixV ().col (1);</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;  Eigen::Vector3f evz = svd.matrixV ().col (2);</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;  Eigen::Vector3f evxminus = evx * -1;</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;  Eigen::Vector3f evyminus = evy * -1;</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;  Eigen::Vector3f evzminus = evz * -1;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160; </div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;  <span class="keywordtype">float</span> s_xplus, s_xminus, s_yplus, s_yminus;</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;  s_xplus = s_xminus = s_yplus = s_yminus = 0.f;</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160; </div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  <span class="comment">//disambiguate rf using all points</span></div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; static_cast&lt;int&gt; (grid-&gt;points.size ()); k++)</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  {</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    Eigen::Vector3f pvector = grid-&gt;points[k].getVector3fMap ();</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="keywordtype">float</span> dist_x, dist_y;</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    dist_x = std::abs (evx.dot (pvector));</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    dist_y = std::abs (evy.dot (pvector));</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160; </div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <span class="keywordflow">if</span> ((pvector).dot (evx) &gt;= 0)</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;      s_xplus += dist_x;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;      s_xminus += dist_x;</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160; </div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="keywordflow">if</span> ((pvector).dot (evy) &gt;= 0)</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;      s_yplus += dist_y;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;      s_yminus += dist_y;</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  }</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160; </div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;  <span class="keywordflow">if</span> (s_xplus &lt; s_xminus)</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    evx = evxminus;</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;  <span class="keywordflow">if</span> (s_yplus &lt; s_yminus)</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    evy = evyminus;</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;  <span class="comment">//select the axis that could be disambiguated more easily</span></div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;  <span class="keywordtype">float</span> fx, fy;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;  <span class="keywordtype">float</span> max_x = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (std::max (s_xplus, s_xminus));</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;  <span class="keywordtype">float</span> min_x = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (std::min (s_xplus, s_xminus));</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;  <span class="keywordtype">float</span> max_y = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (std::max (s_yplus, s_yminus));</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;  <span class="keywordtype">float</span> min_y = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (std::min (s_yplus, s_yminus));</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160; </div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;  fx = (min_x / max_x);</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;  fy = (min_y / max_y);</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160; </div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  Eigen::Vector3f normal3f = Eigen::Vector3f (normal_centroid4f[0], normal_centroid4f[1], normal_centroid4f[2]);</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  <span class="keywordflow">if</span> (normal3f.dot (evz) &lt; 0)</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    evz = evzminus;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  <span class="comment">//if fx/y close to 1, it was hard to disambiguate</span></div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;  <span class="comment">//what if both are equally easy or difficult to disambiguate, namely fy == fx or very close</span></div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160; </div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;  <span class="keywordtype">float</span> max_axis = std::max (fx, fy);</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;  <span class="keywordtype">float</span> min_axis = std::min (fx, fy);</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160; </div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;  <span class="keywordflow">if</span> ((min_axis / max_axis) &gt; axis_ratio_)</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  {</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;Both axes are equally easy/difficult to disambiguate\n&quot;</span>);</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160; </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    Eigen::Vector3f evy_copy = evy;</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;    Eigen::Vector3f evxminus = evx * -1;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    Eigen::Vector3f evyminus = evy * -1;</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160; </div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    <span class="keywordflow">if</span> (min_axis &gt; min_axis_value_)</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    {</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;      <span class="comment">//combination of all possibilities</span></div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;      Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160; </div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;      evx = evxminus;</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160; </div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;      evx = evy_copy;</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160; </div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;      evx = evyminus;</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160; </div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    }</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    {</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;      <span class="comment">//1-st case (evx selected)</span></div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;      <span class="comment">//2-nd case (evy selected)</span></div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;      evx = evy_copy;</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;      evy = evx.cross (evz);</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;      trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;      transformations.push_back (trans);</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    }</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;  }</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;  {</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <span class="keywordflow">if</span> (fy &lt; fx)</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    {</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;      evx = evy;</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;      fx = fy;</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    }</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160; </div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;    evy = evx.cross (evz);</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    Eigen::Matrix4f trans = createTransFromAxes (evx, evy, evz, transformPC, center_mat);</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    transformations.push_back (trans);</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160; </div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;  }</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160; </div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;}</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160; </div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00375"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#af56442302d5b0f378446895c59358c37">  375</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#af56442302d5b0f378446895c59358c37">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::computeRFAndShapeDistribution</a> (PointInTPtr &amp; processed, <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp; output,</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;                                                                                     std::vector&lt;pcl::PointIndices&gt; &amp; cluster_indices)</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;{</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> ourcvfh_output;</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160; </div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;  cluster_axes_.<a class="code" href="classpcl_1_1_point_cloud.html#a963c0da7320055c79e5af0df4f6ad224">clear</a> ();</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;  cluster_axes_.resize (centroids_dominant_orientations_.size ());</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160; </div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; centroids_dominant_orientations_.size (); i++)</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;  {</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160; </div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    std::vector &lt; Eigen::Matrix4f, Eigen::aligned_allocator&lt;Eigen::Matrix4f&gt; &gt; transformations;</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    PointInTPtr grid (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    sgurf (centroids_dominant_orientations_[i], dominant_normals_[i], processed, transformations, grid, cluster_indices[i]);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160; </div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    <span class="comment">// Make a note of how many transformations correspond to each cluster</span></div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    cluster_axes_[i] = transformations.size ();</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    </div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> t = 0; t &lt; transformations.size (); t++)</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;    {</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160; </div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;      <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a> (*processed, *grid, transformations[t]);</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;      transforms_.push_back (transformations[t]);</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;      valid_transforms_.push_back (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160; </div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;      std::vector &lt; Eigen::VectorXf &gt; quadrants (8);</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;      <span class="keywordtype">int</span> size_hists = 13;</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;      <span class="keywordtype">int</span> num_hists = 8;</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; num_hists; k++)</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;        quadrants[k].setZero (size_hists);</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160; </div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;      Eigen::Vector4f centroid_p;</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;      centroid_p.setZero ();</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;      Eigen::Vector4f max_pt;</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;      <a class="code" href="group__common.html#ga1583a71aef0f54550adef0ebfef89edd">pcl::getMaxDistance</a> (*grid, centroid_p, max_pt);</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;      max_pt[3] = 0;</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;      <span class="keywordtype">double</span> distance_normalization_factor = (centroid_p - max_pt).norm ();</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;      <span class="keywordtype">float</span> hist_incr;</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;      <span class="keywordflow">if</span> (normalize_bins_)</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        hist_incr = 100.0f / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (grid-&gt;points.size () - 1);</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;      <span class="keywordflow">else</span></div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        hist_incr = 1.0f;</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160; </div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;      <span class="keywordtype">float</span> * weights = <span class="keyword">new</span> <span class="keywordtype">float</span>[num_hists];</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;      <span class="keywordtype">float</span> sigma = 0.01f; <span class="comment">//1cm</span></div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;      <span class="keywordtype">float</span> sigma_sq = sigma * sigma;</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 0; k &lt; static_cast&lt;int&gt; (grid-&gt;points.size ()); k++)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;      {</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        Eigen::Vector4f p = grid-&gt;points[k].getVector4fMap ();</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        p[3] = 0.f;</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        <span class="keywordtype">float</span> d = p.norm ();</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        <span class="comment">//compute weight for all octants</span></div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        <span class="keywordtype">float</span> wx = 1.f - std::exp (-((p[0] * p[0]) / (2.f * sigma_sq))); <span class="comment">//how is the weight distributed among two semi-cubes</span></div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;        <span class="keywordtype">float</span> wy = 1.f - std::exp (-((p[1] * p[1]) / (2.f * sigma_sq)));</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        <span class="keywordtype">float</span> wz = 1.f - std::exp (-((p[2] * p[2]) / (2.f * sigma_sq)));</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160; </div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        <span class="comment">//distribute the weights using the x-coordinate</span></div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;        <span class="keywordflow">if</span> (p[0] &gt;= 0)</div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        {</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 3; ii++)</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;            weights[ii] = 0.5f - wx * 0.5f;</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160; </div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 4; ii &lt;= 7; ii++)</div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;            weights[ii] = 0.5f + wx * 0.5f;</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        }</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;        {</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 3; ii++)</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;            weights[ii] = 0.5f + wx * 0.5f;</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160; </div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 4; ii &lt;= 7; ii++)</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;            weights[ii] = 0.5f - wx * 0.5f;</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        }</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160; </div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;        <span class="comment">//distribute the weights using the y-coordinate</span></div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        <span class="keywordflow">if</span> (p[1] &gt;= 0)</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;        {</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 1; ii++)</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;            weights[ii] *= 0.5f - wy * 0.5f;</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 4; ii &lt;= 5; ii++)</div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;            weights[ii] *= 0.5f - wy * 0.5f;</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160; </div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 2; ii &lt;= 3; ii++)</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;            weights[ii] *= 0.5f + wy * 0.5f;</div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160; </div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 6; ii &lt;= 7; ii++)</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;            weights[ii] *= 0.5f + wy * 0.5f;</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;        }</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        {</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 1; ii++)</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;            weights[ii] *= 0.5f + wy * 0.5f;</div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 4; ii &lt;= 5; ii++)</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;            weights[ii] *= 0.5f + wy * 0.5f;</div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160; </div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 2; ii &lt;= 3; ii++)</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;            weights[ii] *= 0.5f - wy * 0.5f;</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160; </div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 6; ii &lt;= 7; ii++)</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;            weights[ii] *= 0.5f - wy * 0.5f;</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        }</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160; </div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <span class="comment">//distribute the weights using the z-coordinate</span></div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        <span class="keywordflow">if</span> (p[2] &gt;= 0)</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;        {</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 7; ii += 2)</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;            weights[ii] *= 0.5f - wz * 0.5f;</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160; </div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 1; ii &lt;= 7; ii += 2)</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;            weights[ii] *= 0.5f + wz * 0.5f;</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160; </div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;        }</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;        {</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 0; ii &lt;= 7; ii += 2)</div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;            weights[ii] *= 0.5f + wz * 0.5f;</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> ii = 1; ii &lt;= 7; ii += 2)</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;            weights[ii] *= 0.5f - wz * 0.5f;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;        }</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160; </div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;        <span class="keywordtype">int</span> h_index = (d &lt;= 0) ? 0 : std::ceil (size_hists * (d / distance_normalization_factor)) - 1;</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;        <span class="comment">/* from http://www.pcl-users.org/OUR-CVFH-problem-td4028436.html</span></div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;<span class="comment">           h_index will be 13 when d is computed on the farthest away point.</span></div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;<span class="comment">          adding the following after computing h_index fixes the problem:</span></div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;<span class="comment">        */</span></div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;        <span class="keywordflow">if</span>(h_index &gt; 12)</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;          h_index = 12;</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; num_hists; j++)</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;          quadrants[j][h_index] += hist_incr * weights[j];</div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160; </div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;      }</div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160; </div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;      <span class="comment">//copy to the cvfh signature</span></div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;      <a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> vfh_signature;</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;      vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (1);</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;      vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> d = 0; d &lt; 308; ++d)</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;        vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0].histogram[d] = output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].histogram[d];</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160; </div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;      <span class="keywordtype">int</span> pos = 45 * 3;</div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;      for (<span class="keywordtype">int</span> k = 0; k &lt; num_hists; k++)</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;      {</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> ii = 0; ii &lt; size_hists; ii++, pos++)</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;        {</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;          vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0].histogram[pos] = quadrants[k][ii];</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        }</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;      }</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160; </div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;      ourcvfh_output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.push_back (vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0]);</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;      ourcvfh_output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = ourcvfh_output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ();</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;      <span class="keyword">delete</span>[] weights;</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    }</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;  }</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160; </div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;  <span class="keywordflow">if</span> (ourcvfh_output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;  {</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    ourcvfh_output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;  }</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;  output = ourcvfh_output;</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;}</div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160; </div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>InT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>NT, <span class="keyword">typename</span> Po<span class="keywordtype">int</span>OutT&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00543"></a><span class="lineno"><a class="line" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a93d8ef40eeb4bd7c655d94b724116147">  543</a></span>&#160;<a class="code" href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a93d8ef40eeb4bd7c655d94b724116147">pcl::OURCVFHEstimation&lt;PointInT, PointNT, PointOutT&gt;::computeFeature</a> (<a class="code" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;{</div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;  <span class="keywordflow">if</span> (refine_clusters_ &lt;= 0.f)</div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    refine_clusters_ = 1.f;</div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160; </div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;  <span class="comment">// Check if input was set</span></div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;  <span class="keywordflow">if</span> (!normals_)</div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;  {</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::computeFeature] No input dataset containing normals was given!\n&quot;</span>, getClassName ().c_str ());</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;  }</div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;  <span class="keywordflow">if</span> (normals_-&gt;points.size () != surface_-&gt;points.size ())</div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;  {</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;[pcl::%s::computeFeature] The number of points in the input dataset differs from the number of points in the dataset containing the normals!\n&quot;</span>, getClassName ().c_str ());</div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 0;</div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.clear ();</div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;  }</div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160; </div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;  centroids_dominant_orientations_.clear ();</div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;  clusters_.clear ();</div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;  transforms_.clear ();</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;  dominant_normals_.clear ();</div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160; </div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;  <span class="comment">// ---[ Step 0: remove normals with high curvature</span></div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;  std::vector&lt;int&gt; indices_out;</div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;  std::vector&lt;int&gt; indices_in;</div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;  filterNormalsWithHighCurvature (*normals_, *indices_, indices_out, indices_in, curv_threshold_);</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160; </div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;  pcl::PointCloud&lt;pcl::PointNormal&gt;::Ptr normals_filtered_cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointNormal&gt;</a> ());</div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (indices_in.size ());</div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;  normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a>);</div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160; </div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;  std::vector&lt;int&gt; indices_from_nfc_to_indices;</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;  indices_from_nfc_to_indices.resize (indices_in.size ());</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160; </div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; indices_in.size (); ++i)</div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;  {</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x = surface_-&gt;points[indices_in[i]].x;</div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y = surface_-&gt;points[indices_in[i]].y;</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;    normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z = surface_-&gt;points[indices_in[i]].z;</div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <span class="comment">//normals_filtered_cloud-&gt;points[i].getNormalVector4fMap() = normals_-&gt;points[indices_in[i]].getNormalVector4fMap();</span></div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;    indices_from_nfc_to_indices[i] = indices_in[i];</div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;  }</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160; </div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;  std::vector&lt;pcl::PointIndices&gt; clusters;</div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160; </div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;  <span class="keywordflow">if</span> (normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () &gt;= min_points_)</div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;  {</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="comment">//recompute normals and use them for clustering</span></div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    {</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;      KdTreePtr normals_tree_filtered (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointNormal&gt;</a> (<span class="keyword">false</span>));</div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;      normals_tree_filtered-&gt;setInputCloud (normals_filtered_cloud);</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;      <a class="code" href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation&lt;PointNormal, PointNormal&gt;</a> n3d;</div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;      n3d.<a class="code" href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">setRadiusSearch</a> (radius_normals_);</div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;      n3d.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (normals_tree_filtered);</div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;      n3d.<a class="code" href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">setInputCloud</a> (normals_filtered_cloud);</div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;      n3d.<a class="code" href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">compute</a> (*normals_filtered_cloud);</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;    }</div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160; </div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;    KdTreePtr normals_tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointNormal&gt;</a> (<span class="keyword">false</span>));</div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    normals_tree-&gt;setInputCloud (normals_filtered_cloud);</div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160; </div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    extractEuclideanClustersSmooth (*normals_filtered_cloud, *normals_filtered_cloud, cluster_tolerance_, normals_tree, clusters,</div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;                                    eps_angle_threshold_, <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (min_points_));</div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160; </div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    std::vector&lt;pcl::PointIndices&gt; clusters_filtered;</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    <span class="keywordtype">int</span> cluster_filtered_idx = 0;</div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; clusters.size (); i++)</div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;    {</div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160; </div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> pi;</div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> pi_cvfh;</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> pi_filtered;</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160; </div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;      clusters_.push_back (pi);</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;      clusters_filtered.push_back (pi_filtered);</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160; </div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;      Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;      Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160; </div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; clusters[i].indices.size (); j++)</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;      {</div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;        avg_normal += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getNormalVector4fMap ();</div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;        avg_centroid += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getVector4fMap ();</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;      }</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160; </div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;      avg_normal /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;      avg_centroid /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;      avg_normal.normalize ();</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160; </div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;      Eigen::Vector3f avg_norm (avg_normal[0], avg_normal[1], avg_normal[2]);</div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;      Eigen::Vector3f avg_dominant_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);</div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160; </div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; clusters[i].indices.size (); j++)</div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;      {</div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;        <span class="comment">//decide if normal should be added</span></div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;        <span class="keywordtype">double</span> dot_p = avg_normal.dot (normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getNormalVector4fMap ());</div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;        <span class="keywordflow">if</span> (fabs (acos (dot_p)) &lt; (eps_angle_threshold_ * refine_clusters_))</div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;        {</div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;          clusters_[cluster_filtered_idx].indices.push_back (indices_from_nfc_to_indices[clusters[i].indices[j]]);</div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;          clusters_filtered[cluster_filtered_idx].indices.push_back (clusters[i].indices[j]);</div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;        }</div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;      }</div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160; </div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;      <span class="comment">//remove last cluster if no points found...</span></div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;      <span class="keywordflow">if</span> (clusters_[cluster_filtered_idx].indices.size () == 0)</div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;      {</div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;        clusters_.pop_back ();</div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;        clusters_filtered.pop_back ();</div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;      }</div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;      <span class="keywordflow">else</span></div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;        cluster_filtered_idx++;</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;    }</div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160; </div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;    clusters = clusters_filtered;</div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160; </div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;  }</div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160; </div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;  <a class="code" href="classpcl_1_1_v_f_h_estimation.html">pcl::VFHEstimation&lt;PointInT, PointNT, pcl::VFHSignature308&gt;</a> vfh;</div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;  vfh.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (surface_);</div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;  vfh.<a class="code" href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">setInputNormals</a> (normals_);</div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;  vfh.<a class="code" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (indices_);</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;  vfh.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (this-&gt;tree_);</div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">setUseGivenNormal</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#afe8d57987178f9ac5b9a220dbca2e02f">setUseGivenCentroid</a> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;  vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#ac2b2d9368996748a9ea3b5250976ad01">setNormalizeBins</a> (normalize_bins_);</div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;  output.<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160; </div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;  <span class="comment">// ---[ Step 1b : check if any dominant cluster was found</span></div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;  <span class="keywordflow">if</span> (clusters.size () &gt; 0)</div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;  { <span class="comment">// ---[ Step 1b.1 : If yes, compute CVFH using the cluster information</span></div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160; </div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; clusters.size (); ++i) <span class="comment">//for each cluster</span></div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160; </div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;    {</div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;      Eigen::Vector4f avg_normal = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;      Eigen::Vector4f avg_centroid = Eigen::Vector4f::Zero ();</div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160; </div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; clusters[i].indices.size (); j++)</div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;      {</div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;        avg_normal += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getNormalVector4fMap ();</div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;        avg_centroid += normals_filtered_cloud-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[clusters[i].indices[j]].getVector4fMap ();</div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;      }</div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160; </div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;      avg_normal /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;      avg_centroid /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters[i].indices.size ());</div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;      avg_normal.normalize ();</div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160; </div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;      Eigen::Vector3f avg_norm (avg_normal[0], avg_normal[1], avg_normal[2]);</div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;      Eigen::Vector3f avg_dominant_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);</div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160; </div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;      <span class="comment">//append normal and centroid for the clusters</span></div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;      dominant_normals_.push_back (avg_norm);</div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;      centroids_dominant_orientations_.push_back (avg_dominant_centroid);</div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;    }</div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160; </div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;    <span class="comment">//compute modified VFH for all dominant clusters and add them to the list!</span></div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (dominant_normals_.size ());</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (dominant_normals_.size ());</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160; </div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; dominant_normals_.size (); ++i)</div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;    {</div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;      <span class="comment">//configure VFH computation for CVFH</span></div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8bb453b6e0fbae76da240b9205123e1b">setNormalToUse</a> (dominant_normals_[i]);</div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">setCentroidToUse</a> (centroids_dominant_orientations_[i]);</div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;      <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::VFHSignature308&gt;</a> vfh_signature;</div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;      vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">compute</a> (vfh_signature);</div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;      output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i] = vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0];</div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;    }</div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160; </div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;    <span class="comment">//finish filling the descriptor with the shape distribution</span></div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;    PointInTPtr cloud_input (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);</div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;    <a class="code" href="group__common.html#gaa65b1c8d782e7b776ae682679d2d948f">pcl::copyPointCloud</a> (*surface_, *indices_, *cloud_input);</div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    computeRFAndShapeDistribution (cloud_input, output, clusters_); <span class="comment">//this will set transforms_</span></div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;  }</div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;  { <span class="comment">// ---[ Step 1b.1 : If no, compute a VFH using all the object points</span></div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160; </div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;    PCL_WARN(<span class="stringliteral">&quot;No clusters were found in the surface... using VFH...\n&quot;</span>);</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;    Eigen::Vector4f avg_centroid;</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;    <a class="code" href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a> (*surface_, avg_centroid);</div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;    Eigen::Vector3f cloud_centroid (avg_centroid[0], avg_centroid[1], avg_centroid[2]);</div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;    centroids_dominant_orientations_.push_back (cloud_centroid);</div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160; </div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;    <span class="comment">//configure VFH computation using all object points</span></div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">setCentroidToUse</a> (cloud_centroid);</div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">setUseGivenNormal</a> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160; </div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::VFHSignature308&gt;</a> vfh_signature;</div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    vfh.<a class="code" href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">compute</a> (vfh_signature);</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160; </div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (1);</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = 1;</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160; </div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;    output.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0] = vfh_signature.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[0];</div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;    Eigen::Matrix4f <span class="keywordtype">id</span> = Eigen::Matrix4f::Identity ();</div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;    transforms_.push_back (<span class="keywordtype">id</span>);</div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;    valid_transforms_.push_back (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;  }</div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;}</div>
<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160; </div>
<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_OURCVFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::OURCVFHEstimation&lt;T,NT,OutT&gt;;</span></div>
<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160; </div>
<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;<span class="preprocessor">#endif    </span><span class="comment">// PCL_FEATURES_IMPL_OURCVFH_H_</span></div>
<div class="ttc" id="aclasspcl_1_1_feature_from_normals_html_a349685ac9deb723502de9f399d0286dc"><div class="ttname"><a href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">pcl::FeatureFromNormals::setInputNormals</a></div><div class="ttdeci">void setInputNormals(const PointCloudNConstPtr &amp;normals)</div><div class="ttdoc">Provide a pointer to the input dataset that contains the point normals of the XYZ dataset....</div><div class="ttdef"><b>Definition:</b> feature.h:344</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html"><div class="ttname"><a href="classpcl_1_1_feature.html">pcl::Feature</a></div><div class="ttdoc">Feature represents the base feature class. Some generic 3D operations that are applicable to all feat...</div><div class="ttdef"><b>Definition:</b> feature.h:106</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_a44829319486a2dc415a4e068dc55c577"><div class="ttname"><a href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">pcl::Feature::setRadiusSearch</a></div><div class="ttdeci">void setRadiusSearch(double radius)</div><div class="ttdoc">Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...</div><div class="ttdef"><b>Definition:</b> feature.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ace1caca622f06eee8ad1911228324792"><div class="ttname"><a href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">pcl::Feature::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> feature.h:166</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ad5b1fa9612da40e738b1d99252c5ff2f"><div class="ttname"><a href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">pcl::Feature::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Base method for feature estimation for all points given in &lt;setInputCloud (), setIndices ()&gt; using th...</div><div class="ttdef"><b>Definition:</b> feature.hpp:189</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation</a></div><div class="ttdoc">NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point....</div><div class="ttdef"><b>Definition:</b> normal_3d.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html_ac92dbbea9d923754b3d87d981a6bd131"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">pcl::NormalEstimation::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> normal_3d.h:290</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_a25128a5ac4a93f7a7e520f6e70e0abd2"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a25128a5ac4a93f7a7e520f6e70e0abd2">pcl::OURCVFHEstimation::sgurf</a></div><div class="ttdeci">bool sgurf(Eigen::Vector3f &amp;centroid, Eigen::Vector3f &amp;normal_centroid, PointInTPtr &amp;processed, std::vector&lt; Eigen::Matrix4f, Eigen::aligned_allocator&lt; Eigen::Matrix4f &gt; &gt; &amp;transformations, PointInTPtr &amp;grid, pcl::PointIndices &amp;indices)</div><div class="ttdoc">Computes SGURF</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:191</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_a67094d15b4a0e315a1e70294562ec8a7"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a67094d15b4a0e315a1e70294562ec8a7">pcl::OURCVFHEstimation::filterNormalsWithHighCurvature</a></div><div class="ttdeci">void filterNormalsWithHighCurvature(const pcl::PointCloud&lt; PointNT &gt; &amp;cloud, std::vector&lt; int &gt; &amp;indices_to_use, std::vector&lt; int &gt; &amp;indices_out, std::vector&lt; int &gt; &amp;indices_in, float threshold)</div><div class="ttdoc">Removes normals with high curvature caused by real edges or noisy data</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:161</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_a75bbfd331a8bff6f0af23348afc5fe26"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a75bbfd331a8bff6f0af23348afc5fe26">pcl::OURCVFHEstimation::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Overloaded computed method from pcl::Feature.</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:52</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_a93d8ef40eeb4bd7c655d94b724116147"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a93d8ef40eeb4bd7c655d94b724116147">pcl::OURCVFHEstimation::computeFeature</a></div><div class="ttdeci">void computeFeature(PointCloudOut &amp;output)</div><div class="ttdoc">Estimate the OUR-CVFH descriptors at a set of points given by &lt;setInputCloud (), setIndices ()&gt; using...</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:543</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_a9b8d59946380129bb1452193d7629705"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#a9b8d59946380129bb1452193d7629705">pcl::OURCVFHEstimation::extractEuclideanClustersSmooth</a></div><div class="ttdeci">void extractEuclideanClustersSmooth(const pcl::PointCloud&lt; pcl::PointNormal &gt; &amp;cloud, const pcl::PointCloud&lt; pcl::PointNormal &gt; &amp;normals, float tolerance, const pcl::search::Search&lt; pcl::PointNormal &gt;::Ptr &amp;tree, std::vector&lt; pcl::PointIndices &gt; &amp;clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits&lt; int &gt;::max)())</div><div class="ttdoc">Region growing method using Euclidean distances and neighbors normals to add points to a region.</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:75</div></div>
<div class="ttc" id="aclasspcl_1_1_o_u_r_c_v_f_h_estimation_html_af56442302d5b0f378446895c59358c37"><div class="ttname"><a href="classpcl_1_1_o_u_r_c_v_f_h_estimation.html#af56442302d5b0f378446895c59358c37">pcl::OURCVFHEstimation::computeRFAndShapeDistribution</a></div><div class="ttdeci">void computeRFAndShapeDistribution(PointInTPtr &amp;processed, PointCloudOut &amp;output, std::vector&lt; pcl::PointIndices &gt; &amp;cluster_indices)</div><div class="ttdoc">Computes SGURF and the shape distribution based on the selected SGURF</div><div class="ttdef"><b>Definition:</b> our_cvfh.hpp:375</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_ab219359de6eb34c9d51e2e976dd1a0d1"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">pcl::PCLBase::setIndices</a></div><div class="ttdeci">virtual void setIndices(const IndicesPtr &amp;indices)</div><div class="ttdoc">Provide a pointer to the vector of indices that represents the input data.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:73</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a963c0da7320055c79e5af0df4f6ad224"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a963c0da7320055c79e5af0df4f6ad224">pcl::PointCloud::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Removes all points in a cloud and sets the width and height to 0.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:575</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html">pcl::VFHEstimation</a></div><div class="ttdoc">VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...</div><div class="ttdef"><b>Definition:</b> vfh.h:72</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a65c5f646bfbe38de7ea99e86f118a1c1"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a65c5f646bfbe38de7ea99e86f118a1c1">pcl::VFHEstimation::setUseGivenNormal</a></div><div class="ttdeci">void setUseGivenNormal(bool use)</div><div class="ttdoc">Set use_given_normal_</div><div class="ttdef"><b>Definition:</b> vfh.h:145</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a7fceb91de436785e2f00ef1184416956"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a7fceb91de436785e2f00ef1184416956">pcl::VFHEstimation::setCentroidToUse</a></div><div class="ttdeci">void setCentroidToUse(const Eigen::Vector3f &amp;centroid)</div><div class="ttdoc">Set centroid_to_use_</div><div class="ttdef"><b>Definition:</b> vfh.h:174</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a8ad7f79ee618d1a6f9bca5d579c36130"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a8ad7f79ee618d1a6f9bca5d579c36130">pcl::VFHEstimation::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Overloaded computed method from pcl::Feature.</div><div class="ttdef"><b>Definition:</b> vfh.hpp:65</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_a8bb453b6e0fbae76da240b9205123e1b"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#a8bb453b6e0fbae76da240b9205123e1b">pcl::VFHEstimation::setNormalToUse</a></div><div class="ttdeci">void setNormalToUse(const Eigen::Vector3f &amp;normal)</div><div class="ttdoc">Set the normal to use</div><div class="ttdef"><b>Definition:</b> vfh.h:155</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_ac2b2d9368996748a9ea3b5250976ad01"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#ac2b2d9368996748a9ea3b5250976ad01">pcl::VFHEstimation::setNormalizeBins</a></div><div class="ttdeci">void setNormalizeBins(bool normalize)</div><div class="ttdoc">set normalize_bins_</div><div class="ttdef"><b>Definition:</b> vfh.h:183</div></div>
<div class="ttc" id="aclasspcl_1_1_v_f_h_estimation_html_afe8d57987178f9ac5b9a220dbca2e02f"><div class="ttname"><a href="classpcl_1_1_v_f_h_estimation.html#afe8d57987178f9ac5b9a220dbca2e02f">pcl::VFHEstimation::setUseGivenCentroid</a></div><div class="ttdeci">void setUseGivenCentroid(bool use)</div><div class="ttdoc">Set use_given_centroid_</div><div class="ttdef"><b>Definition:</b> vfh.h:164</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_a441f41e648d284d68e1f2015d40f5e7c"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">pcl::search::Search::radiusSearch</a></div><div class="ttdeci">virtual int radiusSearch(const PointT &amp;point, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const =0</div><div class="ttdoc">Search for all the nearest neighbors of the query point in a given radius.</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_ac4a83e895b2a11e89319673117a927fa"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">pcl::search::Search::getInputCloud</a></div><div class="ttdeci">virtual PointCloudConstPtr getInputCloud() const</div><div class="ttdoc">Get a pointer to the input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> search.h:125</div></div>
<div class="ttc" id="agroup__common_html_ga1583a71aef0f54550adef0ebfef89edd"><div class="ttname"><a href="group__common.html#ga1583a71aef0f54550adef0ebfef89edd">pcl::getMaxDistance</a></div><div class="ttdeci">void getMaxDistance(const pcl::PointCloud&lt; PointT &gt; &amp;cloud, const Eigen::Vector4f &amp;pivot_pt, Eigen::Vector4f &amp;max_pt)</div><div class="ttdoc">Get the point at maximum distance from a given point and a given pointcloud</div><div class="ttdef"><b>Definition:</b> common.hpp:130</div></div>
<div class="ttc" id="agroup__common_html_ga52d532f7f2b4d7bba78d13701d3a33d8"><div class="ttname"><a href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a></div><div class="ttdeci">void transformPointCloud(const pcl::PointCloud&lt; PointT &gt; &amp;cloud_in, pcl::PointCloud&lt; PointT &gt; &amp;cloud_out, const Eigen::Transform&lt; Scalar, 3, Eigen::Affine &gt; &amp;transform, bool copy_all_fields=true)</div><div class="ttdoc">Apply an affine transform defined by an Eigen Transform</div><div class="ttdef"><b>Definition:</b> transforms.hpp:42</div></div>
<div class="ttc" id="agroup__common_html_ga7f82fbd4e17063ab86287a2543bdea88"><div class="ttname"><a href="group__common.html#ga7f82fbd4e17063ab86287a2543bdea88">pcl::demeanPointCloud</a></div><div class="ttdeci">void demeanPointCloud(ConstCloudIterator&lt; PointT &gt; &amp;cloud_iterator, const Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid, pcl::PointCloud&lt; PointT &gt; &amp;cloud_out, int npts=0)</div><div class="ttdoc">Subtract a centroid from a point cloud and return the de-meaned representation</div><div class="ttdef"><b>Definition:</b> centroid.hpp:631</div></div>
<div class="ttc" id="agroup__common_html_gaa65b1c8d782e7b776ae682679d2d948f"><div class="ttname"><a href="group__common.html#gaa65b1c8d782e7b776ae682679d2d948f">pcl::copyPointCloud</a></div><div class="ttdeci">PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &amp;cloud_in, const std::vector&lt; int &gt; &amp;indices, pcl::PCLPointCloud2 &amp;cloud_out)</div><div class="ttdoc">Extract the indices of a given point cloud as a new point cloud</div></div>
<div class="ttc" id="agroup__common_html_gaf5729fae15603888b49743b118025290"><div class="ttname"><a href="group__common.html#gaf5729fae15603888b49743b118025290">pcl::compute3DCentroid</a></div><div class="ttdeci">unsigned int compute3DCentroid(ConstCloudIterator&lt; PointT &gt; &amp;cloud_iterator, Eigen::Matrix&lt; Scalar, 4, 1 &gt; &amp;centroid)</div><div class="ttdoc">Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.</div><div class="ttdef"><b>Definition:</b> centroid.hpp:50</div></div>
<div class="ttc" id="astructpcl_1_1_point_indices_html"><div class="ttname"><a href="structpcl_1_1_point_indices.html">pcl::PointIndices</a></div><div class="ttdef"><b>Definition:</b> PointIndices.h:13</div></div>
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